Action recognition is a fundamental task in the video understanding area.
Currently, there are two types of end-to-end action recognition methods: 3D CNN-based methods and 2D CNN-based methods.
3D CNNs are natural extensions of their 2D counterparts and are intuitive spatiotemporal networks that directly tackle 3D volumetric video data [1]}, [2]} but have a high computational cost.
Other alternative efficient architectures, such as TSM [3]}, TEI [4]}, MVFNet [5]}, DSANet [6]}, etc, have been developed to capture temporal information with reasonable training resources.
These methods aim to design efficient temporal modules to perform efficient temporal modeling.
There is also ongoing research into dynamic inference [7]}, adaptive frame sampling techniques [8]}, [9]}, which we believe can complement the end-to-end video recognition approaches.
